Robotic Picking Test Interactions for New Object Pickability
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Solution Overview
Problem
Robotic devices in warehouse environments face challenges in picking up objects due to limitations in perception and decision-making, leading to frequent human intervention and potential damage or downtime, especially when encountering new or changing objects with uncertain pickability.
Innovation Solution
A method of performing test interactions with robotic devices to collect data on objects through trial processes, including visual analysis and subjecting objects to test stimuli, to determine their pickability and guide future interactions, thereby reducing the risk of damage and downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robotic devices are programmed to perform repetitive tasks without human intervention, then productivity increases, but reliability decreases due to frequent getting stuck and requiring human intervention
Solution Approach 1:
The system performs preliminary testing and data collection on new objects before full-scale automated operation. The robotic device collects physical property data through test interactions, builds predictive models, and validates pickability criteria in advance, allowing reliable automated operation without frequent human intervention.
Solution Approach 2:
The system implements continuous feedback loops where test results from object interactions are used to update predictive models and refine pickability criteria. Success and failure data are fed back into the system to improve future decision-making, enabling the robot to adapt and maintain high reliability autonomously.
2Adaptability or versatility
If robotic devices attempt to pick up new objects without prior knowledge, then adaptability improves, but the risk of damage and downtime increases
Solution Approach 1:
Before attempting to pick up new objects in production, the system performs preliminary test interactions to collect physical property data. Objects are characterized through controlled testing, and pickability is predicted in advance, reducing the risk of damage during normal operation.
Solution Approach 2:
The system prepares predictive models and safety criteria in advance based on test data. By pre-establishing knowledge about object properties and successful pick parameters, the system cushions against potential damage risks before they occur in automated operation.
3Measurement precision
If extensive testing and data collection is performed on new objects, then pickability accuracy improves, but time consumption increases
Solution Approach 1:
The system performs a focused set of essential test interactions that collect sufficient data for accurate pickability prediction without exhaustive testing. By identifying and measuring only the critical physical properties needed for successful picking, the system achieves high accuracy while minimizing time consumption.
Solution Approach 2:
The system dynamically adjusts testing parameters and data collection depth based on object characteristics and confidence levels. For well-understood object types, less testing is performed; for novel or complex objects, more comprehensive testing is conducted, optimizing the balance between accuracy and time efficiency.
Data Source
AI summary
Various embodiments of the technology described herein generally relate to robotic systems for interacting with objects in a warehouse environment. More specifically, certain embodiments relate to systems and methods for collecting data related to robotic picking of objects through test interactions. In some embodiments, a robotic device may work in collaboration with a computer vision system for collecting data related to new objects in a warehouse, commercial, industrial, or similar environment. A robotic picking system may operate in a data collection mode during which objects are sent to a robotic picking device for data collection during one or more test interactions or test stimuli. The test interactions and stimuli may be used to produce a whitelist of objects that the robotic picking device may attempt to pick up during regular operation.